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Abstract

Time series from a nocturnal boundary layer are analyzed using fractal
techniques. The behavior of the self-affine fractal dimension, D A , is
found to drop during a gravity wave train and rise with turbulence. D A is
proposed as a time series conditional sampling criterion for
distinguishing waves from turbulence. Only weak correlations are found
between DA and bulk turbulence measures such as Brunt-Vaisala frequency,
Richardson number, and buoyancy length. The advantages of DA analysis over
turbulent kinetic energy (TKE), its component variances, FFT spectra, and
self-similar fractals are also discussed in terms of local versus global
basis functions, dimensional suitability, noise, algorithmic complexity,
and other factors. DA was found to be the only measure capable of reliably
distinguishing the wave from turbulence.